Healthcare is one of the most important aspects of human life. Heart disease is known to be one of the deadliest diseases that is hampering many people around the world. Heart disease must be detected early so the loss of lives can be prevented. The availability of large-scale data for medical diagnosis has helped developed complex machine learning and deep learning-based models for automated early diagnosis of heart diseases. The classical approaches have been limited in terms of not generalizing well to new data which have not been seen in the training set. This is indicated by a large gap in training and test accuracies. In this project, we built quantum-classifiers for heart disease prediction.
What it does
Predicts whether a person is having heart disease or not
How we built it
We trained classical models (ANN, CNN, Naive Bayes, Decision Tree, etc) and quantum-classifiers (using Qiskit and Pennylane)
Challenges we ran into
Optimizing circuit, improving accuracy, the right set of parameters, data mapping to quantum
Accomplishments that we're proud of
We were able to achieve an accuracy of more than 70% using quantum-classifiers, and with future improvement in quantum algorithms we can achieve better results
What we learned
How to train quantum-classifier using Qiskit/Pennylane
What's next for QHeart
More optimized circuits and parameters for better results, using image data also to provide more useful outcomes through a UI which can be easily used by medical professionals.